Convolutional Neural Networks for Passive Monitoring of a Shallow Water Environment using a Single Sensor
This provides a cost-effective solution for remote monitoring of protected marine areas, though it is an incremental improvement over existing sonar techniques.
The paper tackled the problem of passive monitoring of marine vessels in shallow water using a single hydrophone, and showed that convolutional neural networks (CNNs) applied to cepstrum data can detect and estimate vessel ranges at greater distances than conventional methods.
A cost effective approach to remote monitoring of protected areas such as marine reserves and restricted naval waters is to use passive sonar to detect, classify, localize, and track marine vessel activity (including small boats and autonomous underwater vehicles). Cepstral analysis of underwater acoustic data enables the time delay between the direct path arrival and the first multipath arrival to be measured, which in turn enables estimation of the instantaneous range of the source (a small boat). However, this conventional method is limited to ranges where the Lloyd's mirror effect (interference pattern formed between the direct and first multipath arrivals) is discernible. This paper proposes the use of convolutional neural networks (CNNs) for the joint detection and ranging of broadband acoustic noise sources such as marine vessels in conjunction with a data augmentation approach for improving network performance in varied signal-to-noise ratio (SNR) situations. Performance is compared with a conventional passive sonar ranging method for monitoring marine vessel activity using real data from a single hydrophone mounted above the sea floor. It is shown that CNNs operating on cepstrum data are able to detect the presence and estimate the range of transiting vessels at greater distances than the conventional method.